Age-Classification-SigLIP2
| Property | Value |
|---|---|
| Base Model | google/siglip2-base-patch16-224 |
| Task Type | Single-label Image Classification |
| Model Architecture | SiglipForImageClassification |
| Hugging Face URL | Model Repository |
What is Age-Classification-SigLIP2?
Age-Classification-SigLIP2 is a specialized vision-language encoder model fine-tuned from SigLIP2-base for accurate age group classification. The model demonstrates exceptional performance in categorizing images into five distinct age groups, with an overall accuracy of 91.09%. It particularly excels in identifying children (0-12 years) with a remarkable precision of 97.44%.
Implementation Details
The model utilizes the SiglipForImageClassification architecture and processes 224x224 pixel images. It implements five classification categories: Child (0-12), Teenager (13-20), Adult (21-44), Middle Age (45-64), and Aged (65+). The implementation includes comprehensive error handling and probability-based output for each category.
- Built on google/siglip2-base-patch16-224 architecture
- Supports batch processing with PyTorch tensors
- Includes probability scoring for each age category
- Implements efficient image preprocessing pipeline
Core Capabilities
- High-accuracy age group classification (91.09% overall accuracy)
- Exceptional child detection capability (97.44% precision)
- Robust adult classification (93.97% F1-score)
- Real-time processing with GPU acceleration support
- Easy integration with Transformers pipeline
Frequently Asked Questions
Q: What makes this model unique?
The model's standout feature is its exceptional precision in child age group detection (97.44%) and overall balanced performance across age groups, making it particularly reliable for applications requiring accurate age verification and demographic analysis.
Q: What are the recommended use cases?
The model is ideal for demographic analysis, health and fitness applications, security and access control systems, retail and marketing personalization, and forensics applications. Its high precision makes it particularly suitable for applications where age verification is critical.
